Establishment and validation of nomogram models for overall survival and cancer-specific survival in spindle cell sarcoma patients

Spindle cell sarcoma (SCS) is rare in clinical practice. The objective of this study was to establish nomograms to predict the OS and CSS prognosis of patients with SCS based on the Surveillance, Epidemiology, and End Results (SEER) database. The data of patients with SCS between 2004 and 2020 were extracted from the SEER database and randomly allocated to a training cohort and a validation cohort. Univariate and multivariate Cox regression analyses were used to screen for independent risk factors for both overall survival (OS) and cancer-specific survival (CSS). Nomograms for OS and CSS were established for patients with SCS based on the results of multivariate Cox analysis. Then, we validated the nomograms by the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Finally, Kaplan‒Meier curves and log-rank tests were applied to compare patients with SCS at three different levels and in different treatment groups. A total of 1369 patients with SCS were included and randomly allocated to a training cohort (n = 1008, 70%) and a validation cohort (n = 430, 30%). Age, stage, grade, tumour location, surgery, radiation and diagnosis year were found to be independent prognostic factors for OS by Cox regression analysis, while age, stage, grade, tumour location and surgery were found to be independent prognostic factors for CSS. The nomogram models were established based on the results of multivariate Cox analysis for both OS and CSS. The C-indices of the OS model were 0.76 and 0.77 in the training and validation groups, respectively, while they were 0.76 and 0.78 for CSS, respectively. For OS, the 3- and 5-year AUCs were 0.801 and 0.798, respectively, in the training cohort and 0.827 and 0.799, respectively, in the validation cohort; for CSS, they were 0.809 and 0.786, respectively, in the training cohort and 0.831 and 0.801, respectively, in the validation cohort. Calibration curves revealed high consistency in both OS and CSS between the observed survival and the predicted survival. In addition, DCA was used to analyse the clinical practicality of the OS and CSS nomogram models and revealed that they had good net benefits. Surgery remains the main treatment method for SCS patients. The two nomograms we established are expected to accurately predict the personalized prognosis of SCS patients and may be useful for clinical decision-making.


Data and patients
The data were obtained from the Surveillance, Epidemiology and End Results database.The SEER database includes sociodemographic characteristics, clinical factors, tumour staging, pathological variables, surgical and chemoradiotherapy methods, and prognostic information; this information is publicly accessible, and the author obtained permission.SEER* Stat software (version 8.4.1-March 29, 2023, SEER* Stat software) was used.
A total of 3337 patients with SCS between 2004 and 2020 were identified according to the International Classification of Disease for Oncology 3rd (ICD-O-3) code 8801/3, and SCS was confirmed to be the only primary malignancy.The exclusion criteria were as follows: (1) survival months = 0 or unknown, n = 222; (2) unknown race, n = 22; (2) unknown stage, n = 486; (4) unknown site, n = 51; and (5) unknown grade, n = 1118.Finally, 1438 patients were included in the total cohort.The flowchart of the selection process is shown in Fig. 1.The training cohort (n = 1008, 70%) and validation cohort (n = 430, 30%) were randomly assigned and generated.The SEER database is publicly available, so we did not need the approval of the institutional review board.

Construction and validation of the nomogram
Then, nomogram models were established based on the results of multivariate Cox proportional hazard analysis for both OS (Fig. 2A) and CSS (Fig. 2B).The C-index of the OS predictive model was 0.76 in the training cohort and 0.77 in the validation cohort.For the CSS nomogram, the C-indices were 0.76 and 0.78 in the training cohort and validation cohort, respectively.Then, we evaluated the discriminatory ability of the nomogram by receiver operating characteristic (ROC) curve analysis.For OS, the 3/5-year AUCs were 0.801 and 0.798 in the training cohort (Fig. 3A, B) and 0.827 and 0.799 in the validation cohort (Fig. 3C, D).With respect to the CSS nomogram, the 3-and 5-year AUCs were 0.809 and 0.786, respectively, in the training cohort (Fig. 3E, F) and 0.831 and 0.901, respectively, in the validation cohort (Fig. 3G, H), which suggested the excellent discriminatory power of the models for both OS and CSS.Moreover, the 3-and 5-year calibration curves indicated that the nomogram was effective for both OS and CSS (Fig. 4).In addition, DCA was used to analyse the clinical practicality of the nomogram models in the training and validation cohorts for both OS and CSS; the results indicated that they had good positive and net benefits (Fig. 5).

Risk classification system and Kaplan-Meier analysis of treatment efficacy
A risk classification system using the best cutoff determined by X-tile software was developed to further optimize the clinical application of the nomogram for both OS and CSS.Patients with SCS were categorized into three risk levels: the low-risk group, middle-risk group and high-risk group.We conducted Kaplan-Meier curve and log-rank tests on these groups of patients based on risk stratification.The results demonstrated a consistent decline in survival outcomes, both in overall survival (OS) and cancer-specific survival (CSS), as the risk levels increased, confirming our initial expectations (Fig. 6A-D).
The Kaplan-Meier method was used to assess treatment strategy grouping according to stage (Fig. 7), location (Fig. 8) and grade (Fig. 9).According to our aforementioned findings, surgery and radiotherapy emerged as independent prognostic factors for overall survival (OS).Therefore, we categorized the treatment modalities into four groups: no treatment, surgical treatment alone, radiation therapy alone, and combined surgery with radiation therapy.The results revealed that surgical intervention significantly impacted survival outcomes in all subgroups (Figs.7A, B, G, H, 8A, B, G, H, 9A, B, G, H), whereas the impact of radiotherapy was comparatively limited.The clinical benefits of radiotherapy were exclusively observed in the high-grade groups (Fig. 9C, D, I,  J).The combination therapy strategy demonstrated a prognostic advantage (P < 0.01)) in the high-grade subgroup when surgery was combined with adjuvant radiotherapy compared to surgery alone.However, there was www.nature.com/scientificreports/no significant difference (P = 0.16) in clinical benefit between the radiotherapy alone group and the untreated group (Fig. 9E, F, K, L).

Discussion
Recently, there have been increasing reports on the use of nomograms for predicting the prognosis of patients with various sarcomas 18,27,28 .The advantage of this approach is that by combining various independent risk factors based on the patient's condition, the prognosis can be more intuitively evaluated and personalized, and the OS and CSS can be quantified individually, allowing more accurate prognosis prediction 19 .For the first time and by means of this study, nomograms have been established for predicting the prognosis of SCS patients.SCS is an extremely rare sarcoma for which there is almost no clinical evidence indicating prognosis.Therefore, we constructed a nomogram to predict the prognosis of SCS patients.A high area under the ROC curve indicated that the nomogram accurately predicted the probability of 3-OS, 5-OS, or CSS in SCS patients (0.79-0.83).Calibrating the curve indicated a high degree of consistency between the predicted and actual survival rates.Survival analysis according to demographic characteristics indicated that sex and race were not independent prognostic indicators for CSS or OS in patients with SCS, which is consistent with previously published results 21 .By measuring the standard deviation at the nomogram scale, we found that for both OS and CSS, age, stage, grade, location and surgery were the most important prognostic factors.
The prognosis tends to worsen with age, which is consistent with what has been observed in the majority of other cancers.The SEER database defines tumour staging as follows: localized (tumour confined to the organ without invasion of surrounding tissues or lymph node metastasis), regional (tumour invading the organ or www.nature.com/scientificreports/with lymph node metastasis), or distant (distant metastasis).The prognosis of patients with SCS deteriorates as tumour stage progresses, particularly in the distant stage.Subsequent treatment strategy studies will assign the first two groups to the primary group, while the distant group will be allocated to the advanced group.
The histopathological grade of tumours can reflect the degree of abnormality between tumour cells and normal tissues and is an indicator of tumour growth and spread.It is usually closely related to the prognosis in tumour patients 29,30 .Based on our research findings, patients diagnosed with Grade IIII (poorly differentiated) or Grade IV (undifferentiated) gliomas exhibit a significantly inferior prognosis in comparison to those diagnosed with Grade I (well differentiated) or Grade II (moderately differentiated) gliomas.According to the SEER data we extracted, SCS is commonly found primarily in the skin and subcutaneous soft tissues of the limbs and trunk and rarely in organs and deep tissues such as the peritoneum, parotid gland, spleen, liver, heart, and lung.We divided the primary locations of the tumour into superficial and deep tissue.We found that regarding SCS, patients in the superficial tissue group had a better prognosis than those in the deep organ group in terms of both OS and CSS, consistent with the findings of previous research 21 .
Most of the reported cases of SCS involved multimodal treatment, including surgical management, radiation therapy, and chemotherapy 7,[31][32][33] .Surgery is the main treatment method combined with adjuvant therapy, and primary chemotherapy or radiation therapy is generally used only for patients with unresectable or widely metastatic tumours 34,35 .In this study, we concluded that SCS patients in the surgical group achieved good prognostic outcomes in terms of OS and CSS in patients, consistent with the findings of previous studies 21 .This association was found to be particularly pronounced within the primary group as we further stratified the data into subgroups based on stage.Moreover, upon categorizing surgery and radiation therapy into subgroups, we found that combining surgery with adjuvant radiation therapy yielded superior prognostic benefits compared to surgery alone for patients with high tumour grades.The pathological grade of the tumour and the presence of negative or positive margins following surgical resection have been extensively investigated, as they play pivotal roles in determining whether surgical patients should receive adjuvant radiation therapy or chemotherapy [36][37][38] .As the primary site of SCS is distributed throughout the body, it is not feasible to discuss specific surgical, radiotherapy or chemotherapy methods 21 .The present study was stratified based on the depth of the tumour location, and both cohorts demonstrated that surgery was of considerable clinical importance, whereas radiotherapy failed to confer any prognostic benefits.The X-tile algorithm allows us to perform a very reliable analysis of the optimal cutoff point and determine the optimal cutoff value for age 22 as we did in this study.This program can also be used to create a risk stratification system based on survival rate and has been used for survival analysis of many malignant tumours, such as gastric cancer 39 , colon cancer 40 , renal carcinoma 41 , and pancreatic ductal adenocarcinoma 42 .In this study, a risk stratification system with three risk groups consistently showed significant differences in the KM survival curves for both OS and CSS in the training group and validation group, demonstrating the effectiveness of the risk stratification system.
The limitations of this article are summarized as follows: (1) external validation was not conducted and was difficult to achieve due to the extremely low incidence rate of SCS; additional large multicentre studies may need to be performed; (2) the surgical method and chemotherapy regimen were not specified but were classified as "yes" or "no"; This issue must be discussed based on the specific site of the primary tumour, and further research can be conducted; (3) the SEER database does not record smoking history, drinking history or other personal history; moreover, hypertension, diabetes and other basic diseases may influence the prognosis of SCS patients 43 .

Conclusion
In summary, based on the large number of SCS samples in the SEER database, we established and validated new nomograms to predict the prognosis of SCS patients in terms of both OS and CSS via the R package 4.3.0.This research will help doctors more precisely evaluate the prognosis of SCS patients and help in the formulation of treatment strategies.

Figure 4 .Figure 5 .
Figure 4. Calibration plots for 3-and 5-year OS in the training cohort (A,B) and validation cohort (C,D).Calibration plots for 3-and 5-year CSS in the training cohort (E,F) and validation cohort (G,H).

Figure 6 .
Figure 6.K-M curves of OS in the low-, middle-and high-risk groups in the training set (A) and validation set (B). K-M curves of CSS in the low-, middle-and high-risk groups in the training set (C) and validation set (D).

Table 1 .
Baseline demographic and clinical characteristics of SCS patients.

Table 2 .
Univariate and multivariate analysis of OS in the training cohort of SCS patients.

Table 3 .
Univariate and multivariate analysis of CSS in the training cohort of SCS patients.